Lastly, we apply power normalization (i.e. \(\sigma = 0.5\) ) to each element of the spatio-temporal representation as described in [1]}, [2]}. This eventually decreases the sparsity of the final representation and meaningful classification results can be achieved with the classifiers based on inner-product [2]}.
